GPX8 regulates clear cell renal cell carcinoma tumorigenesis through promoting lipogenesis by NNMT

Background Clear cell renal cell carcinoma (ccRCC), with its hallmark phenotype of high cytosolic lipid content, is considered a metabolic cancer. Despite the implication of this lipid-rich phenotype in ccRCC tumorigenesis, the roles and regulators of de novo lipid synthesis (DNL) in ccRCC remain largely unexplained. Methods Our bioinformatic screening focused on ccRCC-lipid phenotypes identified glutathione peroxidase 8 (GPX8), as a clinically relevant upstream regulator of DNL. GPX8 genetic silencing was performed with CRISPR-Cas9 or shRNA in ccRCC cell lines to dissect its roles. Untargeted metabolomics, RNA-seq analyses, and other biochemical assays (e.g., lipid droplets staining, fatty acid uptake, cell proliferation, xenograft, etc.) were carried out to investigate the GPX8s involvement in lipid metabolism and tumorigenesis in ccRCC. The lipid metabolic function of GPX8 and its downstream were also measured by isotope-tracing-based DNL flux measurement. Results GPX8 knockout or downregulation substantially reduced lipid droplet levels (independent of lipid uptake), fatty acid de novo synthesis, triglyceride esterification in vitro, and tumor growth in vivo. The downstream regulator was identified as nicotinamide N-methyltransferase (NNMT): its knockdown phenocopied, and its expression rescued, GPX8 silencing both in vitro and in vivo. Mechanically, GPX8 regulated NNMT via IL6-STAT3 signaling, and blocking this axis suppressed ccRCC survival by activating AMPK. Notably, neither the GPX8-NNMT axis nor the DNL flux was affected by the von Hippel Lindau (VHL) status, the conventional regulator of ccRCC high lipid content. Conclusions Taken together, our findings unravel the roles of the VHL-independent GPX8-NNMT axis in ccRCC lipid metabolism as related to the phenotypes and growth of ccRCC, which may be targeted for therapeutic purposes. Graphical abstractThe research was supported by the Basic Science Research Program (grant NRF-2018R1A3B1052328 to S.P.) funded by the Ministry of Science, Information and Communication Technology, by Future Planning through the National Research Foundation, and by the Basic Science Research Program through the National Research Foundation (NRF-2020R1I1A1A01073124 to J-M.K.) funded by the Ministry of Education of Korea

Observation of tW production in the single-lepton channel in pp collisions at root s=13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at root s = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb(-1) collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t (t) over bar background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 +/- 4 (stat) +/- 12 (syst) pb, consistent with the standard model.Peer reviewe

Measurement and simulation of the background at the CMS muon detectors

The CMS muon system presently consists of three detector technologies equipping different regions of the spectrometer. Drift Tube chambers (DT) are installed in the central region, while Cathode Strip Chambers (CSC) cover the high pseudorapidity regions; both serve as tracking and triggering detectors. Moreover, Resistive Plate Chambers (RPC) complement DT and CSC in barrel and end-caps respectively and are used in the trigger. Finally, Gas Electron Multiplier (GEM) chambers are getting installed in the muon spectrometer end-caps at different stages of the CMS upgrade programme. The study of the different backgrounds the muon detectors are exposed to, is fundamental to assess the system longevity and project its performance to the conditions expected for HL-LHC. In this respect, an accurate modelling of the backgrounds in simulation is of prime importance as many studies rely on simulation-based predictions while these future conditions have never been experienced in reality. The state of the art of the work carried out to understand backgrounds observed with data collected during the LHC runs, as well as at CERN high-intensity gamma irradiation facility, (GIF++), will be presented. Furthermore, the effort made to improve the accuracy of Fluka and GEANT4 based simulations of background will be thoroughly described

Implementation of the CMS-EXO-17-030 analysis in the MadAnalysis 5 framework (production of a pair of resonances decaying each into three jets; 35.9 fb−1^{−1})

We present the MadAnalysis 5 implementation and validation of the CMS-EXO-17-030 search. The search targets pair-produced resonances, each of which decaying into three jets. The results are interpreted within an R-parity violating supersymmetric (RPV SUSY) model, that predicts that pair-produced gluinos decay into three jets. This leads to a six-jet event. For this study, proton–proton collision data which was collected with the CMS detector in 2016 at a center of energy of 13 TeV is used, with a corresponding luminosity of 35.9 fb−1. In the search, the resonance mass is expected to range from 200 GeV to 2000 GeV so that the analysis comprises four signal regions (SRs). To validate the results, we have selected four gluino benchmark masses of 200, 500, 900, and 1600 GeV, each of which being representative of a given signal region (that are denoted SR1, SR2, SR3, and SR4). We have simulated signal events and calculated the signal acceptance within the MadAnalysis 5 framework in each signal region. To validate the recast, our predicted acceptances have been compared with the official values for those benchmark scenarios. An agreement at the level of about 10% has been obtained

Synthesis of heterocyclic-fused benzopyrans via the Pd(II)-catalyzed C–H alkenylation/C–O cyclization of flavones and coumarins

An efficient and practical method for effecting a tandem C-H alkenylation/C-O cyclization has been achieved via the C-H functionalization of flavone derivatives. The synthetic utility of the one-pot sequence was demonstrated by obtaining convenient access to coumarin-annelated benzopyrans. The reaction scope for the transformation was found to be fairly broad, affording good yields of a wide range of flavone-or coumarin-fused benzopyran motifs, which are privileged structures in many biologically active compounds.113131sciescopu

Membrane Contactors for Maximizing Biomethane Recovery in Anaerobic Wastewater Treatments: Recent Efforts and Future Prospect

Increasing demand for water and energy has emphasized the significance of energy-efficient anaerobic wastewater treatment; however, anaerobic effluents still containing a large portion of the total CH4 production are discharged to the environment without being utilized as a valuable energy source. Recently, gas–liquid membrane contactors have been considered as a promising technology to recover such dissolved methane from the effluent due to their attractive characteristics such as high specific mass transfer area, no flooding at high flow rates, and low energy requirement. Nevertheless, the development and further application of membrane contactors were still not fulfilled due to their inherent issues such as membrane wetting and fouling, which lower the CH4 recovery efficiency and thus net energy production. In this perspective, the topics in membrane contactors for dissolved CH4 recovery are discussed in the following order: (1) operational principle, (2) potential as waste-to-energy conversion system, and (3) technical challenges and recent efforts to address them. Then, future efforts that should be devoted to advancing gas–liquid membrane contactors are suggested as concluding remarks

Anticancer Effect of Hemin through ANO1 Inhibition in Human Prostate Cancer Cells

Anoctamin1 (ANO1), a calcium-activated chloride channel, is overexpressed in a variety of cancer cells, including prostate cancer, and is involved in cancer cell proliferation, migration, and invasion. Inhibition of ANO1 in these cancer cells exhibits anticancer effects. In this study, we conducted a screening to identify novel ANO1 inhibitors with anticancer effects using PC-3 human prostate carcinoma cells. Screening of 2978 approved and investigational drugs revealed that hemin is a novel ANO1 inhibitor with an IC50 value of 0.45 μM. Notably, hemin had no significant effect on intracellular calcium signaling and cystic fibrosis transmembrane conductance regulator (CFTR), a cyclic AMP (cAMP)-regulated chloride channel, and it showed a weak inhibitory effect on ANO2 at 3 μM, a concentration that completely inhibits ANO1. Interestingly, hemin also significantly decreased ANO1 protein levels and strongly inhibited the cell proliferation and migration of PC-3 cells in an ANO1-dependent manner. Furthermore, it strongly induced caspase-3 activation, PARP degradation, and apoptosis in PC-3 cells. These findings suggest that hemin possesses anticancer properties via ANO1 inhibition and could be considered for development as a novel treatment for prostate cancer

Stereoselective construction of sterically hindered oxaspirocycles: Via chiral bidentate directing group-mediated C(sp3)-O bond formation

The systematic investigation of chiral bidentate auxiliaries has resulted in the discovery of a chiral 2,2-dimethyl-1-(pyridin-2-yl)propan-1-amine-derived directing group that enables stereoselective palladium(ii)-catalyzed intramolecular C(sp3)-O bond formation. This new chiral directing group exhibited high reactivity in the activation of methylene C(sp3)-H bonds with excellent levels of stereoselectivity (a diastereomeric ratio of up to 39:::1), which allowed the construction of a wide range of oxaspirocycles. Mechanistic investigations were also conducted to elucidate the reaction mechanism and understand the origin of the diastereoselectivity. DFT calculations suggest that only modest levels of diastereoselectivity are accomplished at the rate-determining C-H metalation-deprotonation step and the d.r. is further enriched at the reductive elimination step. © 2018 The Royal Society of Chemistry11sciescopu

Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms

Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information (times) as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm (GA) was applied to the five prediction models to optimize their weights through ensemble techniques and to yield (output) the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average (ARIMA) model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error (RMSE) and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08 (±3.19), 19.25 (±5.28), and 31.30 (±8.81) mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28 (±3.34), 19.99 (±5.59), and 33.13 (±9.27) mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance